{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T16:00:39Z","timestamp":1758816039824,"version":"3.37.3"},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2018,10,10]],"date-time":"2018-10-10T00:00:00Z","timestamp":1539129600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100018044","name":"Verily Life Sciences","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100018044","id-type":"DOI","asserted-by":"crossref"}]},{"name":"LLC"},{"name":"Verily Academic Partnership"},{"DOI":"10.13039\/100007277","name":"Icahn School of Medicine at Mount Sinai","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007277","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"NCATS","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Clinical and Translational Science Award","award":["UL1TR001433-01"],"award-info":[{"award-number":["UL1TR001433-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists\u2019 interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed the Computer-Aided Note and Diagnosis Interface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human interpretation. The annotation app collects classification, segmentation, and image captioning training data, and the evaluation app randomizes the availability of CAD tools to facilitate clinical trials on radiologist enhancement.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Demonstrations and source code are hosted at (https:\/\/candi.nextgenhealthcare.org), and (https:\/\/github.com\/mbadge\/candi), respectively, under GPL-3 license.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary material is available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty855","type":"journal-article","created":{"date-parts":[[2018,10,9]],"date-time":"2018-10-09T19:34:48Z","timestamp":1539113688000},"page":"1610-1612","source":"Crossref","is-referenced-by-count":5,"title":["CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8064-9050","authenticated-orcid":false,"given":"Marcus A","family":"Badgeley","sequence":"first","affiliation":[{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA"},{"name":"Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, USA"},{"name":"Verily Life Sciences LLC, South San Francisco, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manway","family":"Liu","sequence":"additional","affiliation":[{"name":"Verily Life Sciences LLC, South San Francisco, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin S","family":"Glicksberg","sequence":"additional","affiliation":[{"name":"Institute for Computational Health Sciences, University of California, San Francisco, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Shervey","sequence":"additional","affiliation":[{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA"},{"name":"Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Zech","sequence":"additional","affiliation":[{"name":"Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khader","family":"Shameer","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, Northwell Health, Centre for Research Informatics and Innovation, New Hyde Park, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph","family":"Lehar","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Boston University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric K","family":"Oermann","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael V","family":"McConnell","sequence":"additional","affiliation":[{"name":"Verily Life Sciences LLC, South San Francisco, CA, USA"},{"name":"Division of Cardiovascular Medicine, Stanford School of Medicine, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas M","family":"Snyder","sequence":"additional","affiliation":[{"name":"Verily Life Sciences LLC, South San Francisco, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joel T","family":"Dudley","sequence":"additional","affiliation":[{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA"},{"name":"Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,10,10]]},"reference":[{"key":"2023012806493156100_bty855-B1","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2015-010579","article-title":"EHDViz: clinical dashboard development using open-source technologies","volume-title":"BMJ Open","author":"Badgeley","year":"2016"},{"key":"2023012806493156100_bty855-B2","first-page":"1","article-title":"Concurrent computer-aided detection improves reading time of digital breast tomosynthesis and maintains interpretation performance in a multireader multicase study","author":"Benedikt","year":"2017","journal-title":"Am. 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